Original Article Influence of the 2020 Pandemic on Speedway
Total Page:16
File Type:pdf, Size:1020Kb
Journal of Physical Education and Sport ®(JPES),Vol 21 (Supplement issue 2), Art 121 pp 974– 983, Apr.2021 online ISSN: 2247 - 806X; p-ISSN: 2247 – 8051; ISSN - L = 2247 - 8051 © JPES Original Article Influence of the 2020 pandemic on speedway – managerial implication SYLWESTER BEJGER The Faculty of Economic Sciences and Management, Nicolaus Copernicus University, Toruń, POLAND Published online: April 30, 2021 (Accepted for publication April 15, 2021) DOI:10.7752/jpes.2021.s2121 Abstract : The season of the year 2020 was characterized by a number of difficulties and extraordinary organizational and management conditions for many sports disciplines. One of the disciplines most affected by the restrictions caused by the pandemic was motorcycle speedway. Suffice it to say that out of all European leagues, only the Polish league and the Swedish league completed the 2020 season. Moreover in both countries the games started with a long delay and were not preceded by typical preparatory activities for the season, such as sparring matches or individual tournaments. Focusing on Polish highest level league, the PGE Ekstraliga, the paper contains careful statistical and exploratory analysis of data sample encompassing seasons 2015 to 2020, aiming on discovery if and how season 2020 differed from previous ones. This analysis was carried out in the scope of three data sets with different levels of aggregation, concerning meetings, teams and heats of individual riders. The analysis covered the general performance of a whole sample o riders, performance and demographic structure of teams, and results of heat per rider, taking account rider’s nationality and age. The results of the analysis made it possible to identify changes in selected metrics describing the effectiveness of teams and players, probably caused by structural difference of 2020 season. This allows for the formulation of conclusions supporting the management of the teams and clubs in decision making process concerning team’s profile and riders and track preparation. Such conclusions can help coaches mitigate the impact of unfavorable external conditions on team quality and individual player preparation in similar circumstances in a future. Key Words : - team management, motorcycle speedway, data mining, demographic and internationalization, impact of pandemic on sport Introduction Motorcycle speedway (or speedway in short, when misunderstanding is impossible) was created in Australia at the beginning of the 1920s (May, 1978). Speedway in Poland started in 1930 (Błaszkowska, 2018). Currently, speedway in Poland plays an especially important role in the economic, social and sports sense. Contrary to other countries, this sport is very popular in Poland and ranks 9th on the list of sports by popularity (Report, 2021). The meeting of the Polish highest level league, PGE Ekstraliga alone gathered around 700,000 fans in the 2019 season (PGE statistics, 2021). PGE Ekstraliga is considered as one of the most important speedway leagues in a world. Speedway is an individual sport in which trained skill plays a central role, allowing us to understand speedway not as mere sport, but as art (Siitonen et al. 2020). However, the success of the team is the sum of not only the individual efforts of the players, but also depends on the proper strategy of team building and preparation, as well as preparation of a home track. As all of the motorsports, it is an expensive discipline either. There is no doubt that the pandemic season of 2020 has seriously disrupted the functioning of speedway leagues and the training and competition cycle of the riders all over the world. Due to worldwide lockdown only PGE Ekstraliga and Swedish Eliteserien started and finished 2020 season in Europe (season started with significant delay in that countries, 12.06 and 01.08 respectively). In Poland, due to restriction imposed by authorities there were no usual spring, preliminary speedway events (individual tournaments, sparring meetings) and the number of trainings in a track for riders was significantly limited, either. Moreover, even after starting of the league the meetings were held partly without fans and partly with 50% participation of them. Taking into consideration the obvious abnormalities of season 2020 and above mentioned, special importance of a speedway and speedway league for Polish sport and socio-economical life in general, the question of impact of the season’s 2020 conditions on performance of the teams and riders seems to be very important. The main research goal of the paper is to find some distinctive changes of 2020 season’s parameters by mining a statistical data. Such findings could lead to conclusions that could be useful for the management of the teams and decisions making process of building team’s profile, taking into account both demographic aspect and internationalization. As in the other disciplines of sport (see for example Gulak – Lipka, 2020) role of globalization and internationalization in speedway is increasing. Improper management may lead to financial 974--------------------------------------------------------------------------------------------------------------------------------- Corresponding Author: SYLWESTER BEJGER, E-mail: [email protected] SYLWESTER BEJGER --------------------------------------------------------------------------------------------------------------------------------- difficulties, which in turn might result in bankruptcy of the club (Lis, Tomanek, 2020), especially in a macroeconomic crisis environment. Material & methods The following research methods have been used to achieve the assumed research goal: statistical data mining, descriptive, comparative and subject literature analysis. The main body of the research is statistical examination and inference leading to comparative analysis. Time period of the research covers seasons 2015 – 2020 of speedway PGE Ekstraliga in Poland. Author used data 1 collected by the portal http://gurustats.pl . Dataset is divided in three logical parts, namely data on meetings, data on teams and data on riders per meeting. For the purposes of the study, author chose an appropriate subset of the features. Detailed description of variable used in particular part of the research is provided in a Results section. For the research the following exploratory methods have been used. Statistical visualization (histograms, scatter plots and a kernel density estimate (KDE) plot). A histogram aims to approximate the underlying probability density function that generated the data by binning and counting observations. Kernel density estimation (KDE) plot smooths the observations with a Gaussian kernel, producing a continuous density estimate (Hastie, et al. 2009). Selected statistical tests used: D’Agostino and Pearson’s (1973) omnibus test of normality, Epps-Singleton (1986) ES test whether two samples are generated by the same underlying distribution, equality of variance Levene (1960) test. Levene’s test is an alternative to Bartlett’s test in the case where there are significant deviations from normality. In a part of the research connected with cluster analysis the Kruskal-Wallis H-test (2001) was adapted. This test is a non-parametric version of ANOVA and tests the null hypothesis that the population median of all of the groups sampled from are equal. For correlation assessment the Spearman (1903) rank-order correlation coefficient was used. This test is a nonparametric measure of the monotonicity of the relationship between two datasets. Unlike the Pearson correlation, the Spearman correlation does not assume that both datasets are normally distributed. To calculate Spearman rs for a sample of size n, values of variables Xi and Yi are converted to its ranks rg X and rg Y and use to compute: (1) where: cov( rg X, rg Y) is the covariance of the rank variables, rgX , rgY are the standard deviations of the rank variables. Important part of the research is the cluster analysis. For that task K-Means algorithm (1957, 1965) was used. The K-Means algorithm divides a set of ͈ samples ͒ into k disjoint clusters ̽, each described by the mean ͞ of the samples in the cluster. The means are called the cluster “centroids”. The K-Means algorithm aims to choose centroids that minimize the criterion so called inertia, which is within-cluster sum of squares: (2) Inertia can be recognized as a measure of how internally coherent clusters are. The number of clusters k can be determined according to inertia or mean silhouette coefficient over all the instances. An instance's silhouette coefficient is equal to ( ͖−͕)/max( ͕,͖) where ͕ is the mean distance to the other instances in the same cluster (it is the mean intra-cluster distance), and ͖ is the mean nearest-cluster distance, that is the mean distance to the instances of the next closest cluster (defined as the one that minimizes ͖, excluding the instance's own cluster). The silhouette coefficient can vary between -1 and +1: a coefficient close to +1 means that the instance is well inside its own cluster and far from other clusters, while a coefficient close to 0 means that it is close to a cluster boundary, and finally a coefficient close to -1 means that the instance may have been assigned to the wrong cluster. Results The first part of the empirical research starts with analysis of the meetings in a sample period. The main research question was whether the 2020 season was statistically significantly different from other seasons in terms of overall results and general performance